Spatial spreading in a multi-antenna communication system

ABSTRACT

Spatial spreading is performed in a multi-antenna system to randomize an “effective” channel observed by a receiving entity for each transmitted data symbol block. For a MIMO system, at a transmitting entity, data is processed (e.g., encoded, interleaved, and modulated) to obtain N D  data symbol blocks to be transmitted in N M  transmission spans, where N D ≧1 and N M &gt;1. The N D  blocks are partitioned into N M  data symbol subblocks, one subblock for each transmission span. A steering matrix is selected (e.g., in a deterministic or pseudo-random manner from among a set of L steering matrices, where L&gt;1) for each subblock. Each data symbol subblock is spatially processed with the steering matrix selected for that subblock to obtain transmit symbols, which are further processed and transmitted via N T  transmit antennas in one transmission span. The N D  data symbol blocks are thus spatially processed with N M  steering matrices and observe an ensemble of channels.

I. CLAIM OF PRIORITY UNDER 35 U.S.C. §119

This application is a continuation of U.S. patent application Ser. No.14/523,450, entitled “SPATIAL SPREADING IN A MULTI-ANTENNA COMMUNICATIONSYSTEM,” filed Oct. 24, 2014, now allowed, which is a continuation ofU.S. patent application Ser. No. 13/526,160, entitled “SPATIAL SPREADINGIN A MULTI-ANTENNA COMMUNICATION SYSTEM”, filed Jun. 18, 2012, now U.S.Pat. No. 8,903,016, which is a continuation of U.S. patent applicationSer. No. 11/008,865, entitled “SPATIAL SPREADING IN A MULTI-ANTENNACOMMUNICATION SYSTEM”, filed Dec. 9, 2004, issued, which claims thebenefit of U.S. Provisional Application Ser. No. 60/531,021, entitled“PSEUDO-RANDOM TRANSMIT STEERING IN A MULTI-ANTENNA COMMUNICATIONSYSTEM,” filed Dec. 17, 2003, all of which are incorporated by referenceherein in their entirety.

II. REFERENCE TO CO-PENDING APPLICATIONS FOR PATENT

The present application for patent is related to the followingco-pending U.S. patent applications:

“Data Transmission with Spatial Spreading in a MIMO CommunicationSystem” by Walton et al., having Attorney Docket No. 040057, filedconcurrently herewith, assigned to the assignee hereof, and expresslyincorporated by reference herein; and

“Broadcast Transmission with Spatial Spreading in a Multi-AntennaCommunication System” by Walton et al., having Attorney Docket No.040060, filed concurrently herewith, assigned to the assignee hereof,and expressly incorporated by reference herein.

BACKGROUND Field

The present invention relates generally to data communication, and morespecifically to techniques for transmitting data in a multi-antennacommunication system.

Background

A multiple-input multiple-output (MIMO) communication system employsmultiple (N_(T)) transmit antennas at a transmitting entity and multiple(N_(R)) receive antennas at a receiving entity for data transmission andis denoted as an (N_(T), N_(R)) system. A MIMO channel formed by theN_(T) transmit antennas and the N_(R) receive antennas may be decomposedinto N_(S) spatial channels, where N_(S)≦min {N_(T), N_(R)}. The N_(S)spatial channels may be used to transmit data in a manner to achievegreater reliability and/or higher overall throughput for the system.

The N_(S) spatial channels of the MIMO channel may experience differentchannel conditions (e.g., different fading, multipath, and interferenceeffects) and may achieve different signal-to-noise-and-interferenceratios (SNRs). The SNR of a spatial channel determines its transmissioncapacity, which is typically quantified by a particular data rate thatmay be reliably transmitted on the spatial channel. For a time variantMIMO channel, the channel conditions change over time and the SNR ofeach spatial channel also changes over time. To maximize throughput, theMIMO system may utilize some form of feedback whereby the receivingentity evaluates the spatial channels and provides feedback informationindicating the transmission capacity of each spatial channel. Thetransmitting entity would then adjust data transmission on the spatialchannels based on the feedback information.

However, this feedback information may not be available for variousreasons. For example, the MIMO system may not support transmission offeedback from the receiving entity. As another example, the MIMO channelmay change more rapidly than the rate at which the receiving entity canestimate the channel and/or send the feedback information. In any case,if the transmitting entity does not know the channel conditions, then itmay need to transmit data at a very low rate so that the datatransmission can be reliably decoded by the receiving entity even underthe worst-case channel conditions. The performance of such a systemwould then be dictated by the expected worst-case channel conditions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a process for transmitting data with spatial spreading.

FIG. 2 shows a process for receiving data with spatial spreading.

FIG. 3 shows a transmitting entity and a receiving entity in a MIMOsystem.

FIG. 4 shows the processing units at the transmitting entity.

FIG. 5 shows the processing units at the receiving entity.

FIG. 6 shows a process for generating a set of steering matrices usedfor spatial spreading.

FIG. 7 shows plots of overall spectral efficiency achieved for a 4×4MIMO system.

DETAILED DESCRIPTION

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

Techniques for performing spatial spreading in a multi-antennacommunication system are described herein. The multi-antennacommunication system may be a MIMO system or a multiple-inputsingle-output (MISO) system. Spatial spreading refers to thetransmission of a data symbol (which is a modulation symbol for data)from multiple transmit antennas simultaneously, possibly with differentamplitudes and/or phases determined by a steering vector used for thatdata symbol. Spatial spreading may also be called transmit steering,pseudo-random transmit steering, steering diversity, matrixpseudo-random steering, vector pseudo-random steering, and so on. Thespatial processing techniques can randomize an “effective” MIMO or MISOchannel observed by a receiving entity for each block of data symbolstransmitted by a transmitting entity so that system performance is notdictated by the worst-case channel conditions.

In an embodiment for transmitting data with spatial spreading in a MIMOsystem, the transmitting entity processes (e.g., encodes andinterleaves) data for N_(D) data streams and generates N_(D) blocks ofcoded data, where N_(D)≧1. A block of coded data may also be called acode block or a coded data packet. Each code block is encoded separatelyat the transmitting entity and decoded separately at the receivingentity. Each code block is symbol mapped to obtain a corresponding blockof data symbols. The N_(D) data symbol blocks for the N_(D) code blocksare partitioned into N_(M) data symbol subblocks for transmission inN_(M) transmission spans, one subblock in each transmission span, whereN_(M)>1. A transmission span can cover time and/or frequency dimensions,as described below. A steering matrix is selected (e.g., from among aset of L steering matrices) for each of the N_(M) data symbol subblocks.Each data symbol subblock is spatially processed with the steeringmatrix selected for that subblock to generate transmit symbols, whichare further processed and transmitted via N_(T) transmit antennas in onetransmission span. In effect, the N_(D) data symbol blocks are spatiallyprocessed with N_(M) steering matrices and therefore observe an ensembleof channels as opposed to all blocks observing the same channel. Thesteering matrices used for spatial spreading are unitary matrices havingorthogonal columns or vectors and may be generated as described below.

A MISO system may also transmit data with spatial spreading, asdescribed below. Various aspects and embodiments of the invention aredescribed in further detail below.

The spatial spreading techniques described herein may be used for MIMOand MISO systems. These techniques may also be used for single-carrierand multi-carrier systems. Multiple carriers may be obtained withorthogonal frequency division multiplexing (OFDM), some othermulti-carrier modulation techniques, or some other construct. OFDMeffectively partitions the overall system bandwidth into multiple(N_(F)) orthogonal subbands, which are also referred to as tones,subcarriers, bins, and frequency channels. With OFDM, each subband isassociated with a respective subcarrier that may be modulated with data.

1. MIMO System

For a single-carrier MIMO system, a MIMO channel formed by N_(T)transmit antennas at the transmitting entity and N_(R) receive antennasat the receiving entity may be characterized by an N_(R)×N_(T) channelresponse matrix H, which may be expressed as:

$\begin{matrix}{{\underset{\_}{H} = \begin{bmatrix}h_{1,1} & h_{1,2} & \ldots & h_{1,N_{T}} \\h_{2,1} & h_{2,2} & \ldots & h_{2,N_{T}} \\\vdots & \vdots & \ddots & \vdots \\h_{N_{R},1} & h_{N_{R},2} & \ldots & h_{N_{R},N_{T}}\end{bmatrix}},} & {{Eq}\mspace{14mu} (1)}\end{matrix}$

where entry h_(i,j), for i=1 . . . N_(R) and j=1 . . . N_(T), denotesthe coupling or complex gain between transmit antenna j and receiveantenna i.

Data may be transmitted in various manners in the MIMO system. In onesimple transmission scheme, one data symbol stream is transmitted fromeach transmit antenna without any spatial processing, and up to N_(S)data symbol streams are transmitted simultaneously from the N_(T)transmit antennas. The model for the MIMO system for this transmissionscheme may be expressed as:

r=Hs+n,  Eq (2)

where

-   -   s is an N_(T)×1 vector with N_(S) non-zero entries for N_(S)        data symbols to be transmitted on the N_(S) spatial channels of        H;    -   r is an N_(R)×1 vector with entries for N_(R) received symbols        obtained via the N_(R) receive antennas; and    -   n is a noise vector observed at the receiving entity.        The noise may be assumed to be additive white Gaussian noise        (AWGN) with a zero mean vector and a covariance matrix of        Λ_(n)=σ²I, where σ² is the variance of the noise and I is the        identity matrix.

The N_(S) data symbol streams transmitted from the N_(T) transmitantennas interfere with each other at the receiving entity. A given datasymbol stream transmitted from one transmit antenna is typicallyreceived by all N_(R) receive antennas at different amplitudes andphases. Each received symbol stream includes a component of each of theN_(S) transmitted data symbol streams. The N_(R) received symbol streamswould collectively include all of the N_(S) data symbols streams.However, these N_(S) data symbol streams are dispersed among the N_(R)received symbol streams. The receiving entity performs receiver spatialprocessing on the N_(R) received symbol streams to recover the Ns datasymbol streams sent by the transmitting entity.

The performance that can be achieved for the MIMO system is dependent(to a large extent) on the channel response matrix H. If a high degreeof correlation exists within H, then each data symbol stream wouldobserve a large amount of interference from the other streams. Thisinterference or cross-talk cannot be removed by the spatial processingat the receiving entity. The high level of interference degrades the SNRof each affected data symbol stream, possibly to a point where the datasymbol stream cannot be decoded correctly by the receiving entity.

For a given channel response matrix H, system capacity may be achievedwhen the transmitting entity transmits data on N_(S) eigenmodes (ororthogonal spatial channels) of the MIMO channel using eigenvectorsderived from H. If the receiving entity can provide the transmittingentity with either full or partial Channel State Information (CSI), thenthe transmitting entity can process the data streams in a manner thatmaximizes the overall throughput for these streams (e.g., by using anoptimal or near optimal data rate for each data stream). However, if thetransmitting entity is uninformed or misinformed, then the data rate(s)employed for the data streams may result in frame or code block errorsfor a certain percentage of channel realizations. For example, a “bad”channel response may occur when H exhibits a high degree of correlation,or when there is insufficient scattering, multipath (large coherencebandwidth) and/or temporal fading (large coherence time) in the wirelesschannel. The occurrence of “bad” channels is random and it is desirableto minimize the percentage of time this can occur for a given data rateselection.

For some MIMO systems, performance may be dictated by the worst-casechannel conditions. For example, if the receiving entity cannot sendfeedback information to indicate the proper data rate to use for eachdata symbol stream (e.g., because feedback is not supported by thesystem or the channel conditions change faster than the feedback rate),then the transmitting entity may need to transmit the data symbolstreams at low rates so that these streams can be recovered even underthe worst-case channel conditions. System performance would then bedictated by the expected worst-case channel conditions, which is highlyundesirable.

Spatial spreading may be used to randomize the effective MIMO channelobserved by the receiving entity so that system performance is notdictated by the worst-case channel conditions. With spatial spreading,the transmitting entity performs spatial processing with differentsteering matrices to effectively randomize the MIMO channel so that eachcode block for each data stream observes an ensemble of channels and isnot stuck on a bad channel for an extended period of time.

The spatial processing at the transmitting entity for spatial spreadingmay be expressed as:

x(m)=V(m)·s(m),  Eq (3)

where

-   -   s(m) is an N_(S)×1 vector with N_(S) data symbols to be sent in        transmission span m;    -   V(m) is an N_(T)×N_(S) steering matrix for transmission span m;        and    -   x(m) is an N_(T)×1 vector with N_(T) transmit symbols to be sent        from the N_(T) transmit antennas in transmission span m.        In general, up to N_(S) data symbol streams may be transmitted        simultaneously using the N_(S) spatial channels of H(m). For        simplicity, much of the following description assumes that N_(S)        data symbol streams are transmitted simultaneously.

A transmission span may cover time and/or frequency dimensions. Forexample, in a single-carrier MIMO system, a transmission span maycorrespond to one symbol period, which is the time duration to transmitone data symbol. As another example, in a multi-carrier MIMO system,such as a MIMO system that utilizes OFDM, a transmission span maycorrespond to one subband in one OFDM symbol period. A transmission spanmay also cover multiple symbol periods and/or multiple subbands. Thus, mmay be an index for time and/or frequency. The transmission span mayalso be referred to as a transmission interval, a signaling interval, aslot, and so on.

A set of L steering matrices may be generated as described below andused for spatial spreading. This steering matrix set is denoted as {V},or V(i) for i=1 . . . L, where L may be any integer greater than one.One steering matrix in the set may be selected for each transmissionspan m. The transmitting entity would then perform spatial processingfor each transmission span m with the steering matrix V(m) selected forthat transmission span, where V(m) ε{V}. The results of the spatialprocessing are N_(T) transmit symbol streams, which are furtherconditioned and transmitted from the N_(T) transmit antennas.

The received symbols at the receiving entity with spatial spreading maybe expressed as:

r(m)=H(m)·V(m)·s(m)+n(m)=H _(eff)(m)·s(m)+n(m),  Eq (4)

where

-   -   H(m) is an N_(R)×N_(T) channel response matrix for transmission        span m;    -   H_(eff) (m) is an N_(R)×N_(S) effective channel response matrix        for transmission span m, which is H_(eff)(m)=H(m)·V(m);    -   r(m) is an N_(R)×1 vector with N_(R) received symbols for        transmission span m; and    -   n(m) is a noise vector for transmission span m.

As shown in equation (4), because of the spatial spreading performed bythe transmitting entity, the N_(S) data symbol streams observe theeffective channel response H_(eff) (m) instead of the actual channelresponse H(m). Each data symbol stream is thus sent on a spatial channelof H_(eff) (m) instead of H(m). The steering matrices may be selectedsuch that each data symbol stream observes an ensemble of spatialchannels of H(m). Moreover, if different steering matrices are usedacross a code block, then the data symbols for the code block wouldobserve different channels across the code block.

The receiving entity can perform receiver spatial processing on thereceived symbols with an estimate of the effective channel responsematrix to recover the transmitted data symbol streams. If the receivingentity has knowledge of the steering matrix used by the transmittingentity for each transmission span m, then the receiving entity canestimate the channel response matrix (e.g., based on received pilotsymbols) and compute an estimated effective channel response matrix asĤ_(off) (m)=H(m)·V(m), where “̂” denotes an estimate of the actualmatrix. Alternatively, the receiving entity can directly estimate theeffective channel response matrix, H_(eff) (m), e.g., based on receivedpilot symbols that have been transmitted using V(m). A pilot symbol is amodulation symbol for pilot, which is data that is known a priori byboth the transmitting and receiving entities.

In general, any number of (N_(D)) data streams may be transmittedsimultaneously via the MIMO channel, where N_(S)≧N_(D)≧1. For example,if N_(D)=N_(S), then one data stream may be transmitted on each of theN_(S) spatial channels of H_(eff) (m). If N_(D)=1, then one data streammay be demultiplexed and transmitted on all N_(S) spatial channels ofH_(eff)(m). In any case, each data stream is processed (e.g., encoded,interleaved, and modulated) to obtain data symbols, and the data symbolsfor all N_(D) data streams are demultiplexed into N_(S) data symbolstreams for the N_(S) spatial channels of H_(eff) (m), as describedbelow. A steering matrix is used for spatial processing for onetransmission span, which may cover one or multiple data symbol vectors.

FIG. 1 shows a process 100 for transmitting data with spatial spreading.Initially, data is processed to obtain a set of N_(D) data symbol blocksfor N_(D) data streams, one block for each data stream (block 112). Eachdata symbol block contains data symbols generated from one code block ofcoded data (or one coded data packet). The data processing may beperformed as described below. The N_(D) data symbol blocks arepartitioned into N_(M) data symbol subblocks to be transmitted in N_(M)transmission spans, one subblock in each transmission span (block 114).N_(M) is also referred to as the block length and is N_(M)>1. Eachsubblock may contain one or more data symbols from each of the N_(D)blocks. For example, if N_(D)=N_(S), then each subblock may containN_(S) data symbols from N_(S) blocks for N_(S) data streams. As anotherexample, if N_(D)=1, then each subblock may contain N_(S) data symbolsfrom one block for one data stream. Index m used to denote thetransmission span for the current set of data symbol blocks is set to 1(block 116).

One steering matrix V(m) is used for spatial processing for eachtransmission span m. This steering matrix V(m) may be selected from theset of L steering matrices {V} (block 118). Spatial processing is thenperformed on data symbol subblock m with steering matrix V(m) to obtaintransmit symbols (block 120). If transmission span m covers one datasymbol vector, then one vector s(m) with up to N_(S) data symbols isformed from data symbol subblock m and spatially processed with steeringmatrix V(m) to obtain the corresponding transmit symbol vector x(m), asshown in equation (3). If transmission span m covers multiple (N_(V))data symbol vectors, then N_(V) vectors s_(l)(m), for l=1 . . . N_(V),are formed from data symbol subblock m, and each vector s_(l)(m) isspatially processed with the same steering matrix V(m) to obtain acorresponding transmit symbol vector x_(l)(m). In any case, the samesteering matrix V(m) is used for spatial processing for all data symbolvectors in transmission span m, and the resultant transmit symbolvectors are processed and transmitted via the N_(T) transmit antennas intransmission span m (block 122).

A determination is then made whether the N_(M) data symbol subblockshave been processed and transmitted (i.e., whether m=N_(M)) (block 124).If the answer is ‘No’, then index m is incremented for the nextsubblock/transmission span (block 126), and the process returns to block118. If the answer is ‘Yes’ for block 124, then a determination is madewhether there is more data to transmit (block 128). If the answer is‘Yes’, then the process returns to block 112 to start the processing forthe next set of data symbol blocks. Otherwise, the process terminates.

As shown in FIG. 1, each set of data symbol blocks is spatiallyprocessed with N_(M) steering matrices to obtain N_(T) transmit symbolsequences. Each transmit symbol sequence is transmitted via a respectiveone of the N_(T) transmit antennas in N_(M) transmission spans. TheN_(M) steering matrices randomize the effective MIMO channel observed bythe receiving entity for the N_(D) data symbol blocks. The randomizationof the MIMO channel results from using different steering matrices fordifferent transmission spans and not necessarily from randomness in theelements of the steering matrices.

As noted above, a transmission span can be defined to cover one or moresymbol periods and/or one or more subbands. For improved performance, itis desirable to select the transmission span to be as small as possibleso that (1) more steering matrices can be used for each data symbolblock and (2) the receiving entity can obtain as many “looks” of theMIMO channel as possible for each data symbol block. The transmissionspan should also be shorter than the coherence time of the MIMO channel,which is the time duration over which the MIMO channel can be assumed tobe approximately static. Similarly, the transmission span should besmaller than the coherence bandwidth of the channel for an OFDM-basedsystem.

FIG. 2 shows a process 200 for receiving data with spatial spreading.Initially, index m used to denote the transmission span for the currentset of data symbol blocks is set to 1 (block 212). Received data symbolsare obtained from the N_(R) receive antennas for data symbol subblock m(block 214). Steering matrix V(m) used by the transmitting entity forsubblock m is determined (block 216) and used to derive a channelresponse estimate for the effective MIMO channel observed by subblock m.This channel response estimate is then used to perform receiver spatialprocessing on the received data symbols to obtain detected symbols (ordata symbol estimates) for subblock m (block 218).

A determination is then made whether the N_(M) data symbol subblocks forthe current data symbol block set have been received (i.e., whetherm=N_(M)) (block 220). If the answer is ‘No’, then index m is incrementedfor the next subblock/transmission span (block 222), and the processreturns to block 214. If the answer is ‘Yes’ for block 220, then thedetected symbols for all N_(M) subblocks are processed (e.g.,demodulated, deinterleaved, and decoded) to obtain decoded data for thecurrent data symbol block set (block 224). A determination is then madewhether there is more data to receive (block 226). If the answer is‘Yes’, then the process returns to block 212 to start receiving the nextset of data symbol blocks. Otherwise, the process terminates.

A. Steering Matrix Selection

As noted above, a set of L steering matrices may be generated and usedfor spatial spreading. The steering matrices in the set may be selectedfor use in various manners. In one embodiment, the steering matrices areselected from the set in a deterministic manner. For example, the Lsteering matrices may be cycled through and selected in sequentialorder, starting with the first steering matrix V(1), then the secondsteering matrix V(2), and so on, and then the last steering matrix V(L).In another embodiment, the steering matrices are selected from the setin a pseudo-random manner. For example, the steering matrix to use foreach transmission span m may be selected based on a function ƒ(m) thatpseudo-randomly selects one of the L steering matrices, or steeringmatrix V(ƒ(m)). In yet another embodiment, the steering matrices areselected from the set in a “permutated” manner. For example, the Lsteering matrices may be cycled through and selected for use insequential order. However, the starting steering matrix for each cyclemay be selected in a pseudo-random manner, instead of always being thefirst steering matrix V(1). The L steering matrices may also be selectedin various other manners, and this is within the scope of the invention.

The steering matrix selection may also be dependent on the number ofsteering matrices (L) in the set and the block length (N_(M)). Ingeneral, the number of steering matrices may be greater than, equal to,or less than the block length. Steering matrix selection for these threecases may be performed as described below.

If L=N_(M), then the number of steering matrices matches the blocklength. In this case, a different steering matrix may be selected foreach of the N_(M) transmission spans used to transmit the set of datasymbol blocks. The N_(M) steering matrices for the N_(M) transmissionspans may be selected in a deterministic, pseudo-random, or permutatedmanner, as described above. For example, the L steering matrices in theset may be selected in sequential order for each data symbol block set,with the same (pre-selected) or different (pseudo-randomly selected)starting steering matrix being used for each data symbol block set.

If L<N_(M), then the block length is longer than the number of steeringmatrices in the set. In this case, the steering matrices are reused foreach data symbol block set and may be selected as described above.

If L>N_(M), then a subset of the steering matrices is used for each datasymbol block set. The selection of the specific subset to use for eachdata symbol block set may be deterministic or pseudo-random. Forexample, the first steering matrix to use for the current data symbolblock set may be the steering matrix after the last one used for a priordata symbol block set.

B. System

FIG. 3 shows a block diagram of a transmitting entity 310 and areceiving entity 350 in a MIMO system 300. At transmitting entity 310, atransmit (TX) data processor 320 receives and processes (e.g., encodes,interleaves, and modulates) traffic data for N_(D) data streams andprovides N_(S) data symbol streams, where N_(S)≧N_(D)≧1. A TX spatialprocessor 330 receives and spatially processes the N_(S) data symbolstreams for spatial spreading, multiplexes in pilot symbols, andprovides N_(T) transmit symbol streams to N_(T) transmitter units (TMTR)332 a through 332 t. The processing by TX data processor 320 isdescribed below, and the spatial processing by TX spatial processor 330is as described above. Each transmitter unit 332 conditions (e.g.,converts to analog, filters, amplifies, and frequency upconverts) arespective transmit symbol stream to generate a modulated signal. N_(T)transmitter units 332 a through 332 t provide N_(T) modulated signalsfor transmission from N_(T) antennas 334 a through 334 t, respectively.

At receiving entity 350, N_(R) antennas 352 a through 352 r receive theN_(T) transmitted signals, and each antenna 352 provides a receivedsignal to a respective receiver unit (RCVR) 354. Each receiver unit 354performs processing complementary to the processing performed bytransmitter units 332 and provides (1) received data symbols to areceive (RX) spatial processor 360 and (2) received pilot symbols to achannel estimator 384 within a controller 380. Receive spatial processor360 performs spatial processing on N_(R) received symbol streams fromN_(R) receiver units 354 a through 354 r with channel estimates fromchannel estimator 384 and provides N_(S) detected symbol streams, whichare estimates of the N_(S) data symbol streams sent by transmittingentity 310. An RX data processor 370 then processes (e.g., demaps,deinterleaves, and decodes) the N_(S) detected symbol streams andprovides N_(D) decoded data streams, which are estimates of the N_(D)data streams.

Controllers 340 and 380 control the operation of various processingunits at transmitting entity 310 and receiving entity 350, respectively.Memory units 342 and 382 store data and/or program codes used bycontrollers 340 and 380, respectively.

FIG. 4 shows a block diagram of the processing units at transmittingentity 310. For the embodiment shown in FIG. 4, TX data processor 320includes N_(D) data stream processors 410 a through 410 nd for the N_(D)data streams {d_(l)}, for l=1 . . . N_(D). Within each data streamprocessor 410, an encoder 412 receives and encodes data stream {d_(l)}based on a coding scheme and provides code bits. The coding scheme mayinclude cyclic redundancy check (CRC) generation, convolutional coding,Turbo coding, low density parity check (LDPC) coding, block coding,other coding, or a combination thereof. A channel interleaver 414interleaves (i.e., reorders) the code bits based on an interleavingscheme to achieve frequency, time, and/or spatial diversity. A symbolmapping unit 416 maps the interleaved bits based on a modulation schemeand provides a stream of data symbols {s_(l)}. Unit 416 groups each setof B interleaved bits to form a B-bit value, where B≧1, and further mapseach B-bit value to a specific modulation symbol based on the selectedmodulation scheme (e.g., QPSK, M-PSK, or M-QAM, where M=2^(B)). Theencoding is typically performed separately on each data packet in eachdata stream {d_(l)} to obtain a corresponding coded data packet or codeblock, and the symbol mapping is then performed on each code block toobtain a corresponding data symbol block.

In FIG. 4, N_(D) data stream processors 410 a through 410 nd process theN_(D) data streams and provide N_(D) data symbol blocks for each blocklength of N_(M) transmission spans. One data stream processor 410 mayalso process the N_(D) data streams, e.g., in a time division multiplex(TDM) manner. The same or different coding and modulation schemes may beused for the N_(D) data streams. Furthermore, the same or different datarates may be used for the N_(D) data streams. Amultiplexer/demultiplexer (Mux/Demux) 420 receives andmultiplexes/demultiplexes the data symbols for the N_(D) data streamsinto N_(S) data symbol streams, one data symbol stream for each spatialchannel of H_(eff) (m). If N_(D)=N_(S), then Mux/Demux 420 can simplyprovide the data symbols for each data stream as one data symbol stream.If N_(D)=1, then Mux/Demux 420 demultiplexes the data symbols for theone data stream into N_(S) data symbol streams.

TX spatial processor 330 receives N_(S) data symbol blocks from TX dataprocessor 320 and N_(M) steering matrices V(m) from controller 340 foreach block length of N_(M) transmission spans. The steering matrices maybe retrieved from a steering matrix (SM) storage 442 within memory unit342 or generated by controller 340 as they are needed. TX spatialprocessor 330 performs spatial processing on the data symbols for eachtransmission span m with the steering matrix V(m) for that transmissionspan and provides transmit symbols for the transmission span. TX spatialprocessor 330 multiplexes the transmit symbols for each transmissionspan m to obtain N_(T) transmit symbol sequences, which are to be sentfrom the N_(T) transmit antennas in one or more symbol periods and/or onone or more subbands. TX spatial processor 330 further multiplexes theN_(T) transmit symbol sequences for different transmission spans andprovides N_(T) transmit symbol streams, {x_(j)} for j=1 . . . N_(T) forthe N_(T) transmit antennas.

FIG. 5 shows a block diagram of the processing units at receiving entity350. N_(R) receiver units 354 a through 354 r provide received pilotsymbols, {r_(i) ^(p)} for i=1 . . . N_(R), to channel estimator 384. Inone embodiment, channel estimator 384 derives Ĥ(m), which is an estimateof the channel response matrix H(m), based on the received pilotsymbols. Channel estimator 384 further receives the steering matrix V(m)for each transmission span m and derives the estimated effective channelresponse matrix as Ĥ_(eff) (m)=Ĥ(m)·V(m). For this embodiment, thereceiving and transmitting entities are synchronized so that bothentities use the same steering matrix V(m) for each transmission span m.In another embodiment, channel estimator 384 directly derivesĤ_(eff)(m), which is an estimate of the effective channel responsematrix H_(eff) (m), based on the received pilot symbols. For bothembodiments, channel estimator 384 provides the estimated effectivechannel response matrix Ĥ_(eff)(m) to RX spatial processor 360.

RX spatial processor 360 also obtains received data symbols, {r_(i)^(d)} for i=1 . . . N_(R), from N_(R) receiver units 354 a through 354r. RX spatial processor 360 performs receiver spatial processing on thereceived data symbols with Ĥ_(eff)(m) and using any one of a number ofreceiver spatial processing techniques known in the art. RX spatialprocessor 360 provides detected symbols (or data symbol estimates) to RXdata processor 370.

For the embodiment shown in FIG. 5, RX data processor 370 includes amultiplexer/demultiplexer (Mux/Demux) 508 and N_(D) data streamprocessors 510 a through 510 nd for the N_(D) data streams. Mux/Demux508 receives and multiplexes/demultiplexes the N_(S) detected symbolstreams for the N_(S) spatial channels of H_(eff)(m) into N_(D) detectedsymbol streams for the N_(D) data streams. Mux/Demux 508 operates in amanner complementary to Mux/Demux 420 at transmitting entity 310 in FIG.4. Within each data stream processor 510, a symbol demapping unit 512demodulates the detected symbols for the associated data stream inaccordance with the modulation scheme used for that stream and providesdemodulated data. A channel deinterleaver 514 deinterleaves thedemodulated data in a manner complementary to the interleaving performedon that stream by transmitting entity 310. A decoder 516 then decodesthe deinterleaved data in a manner complementary to the encodingperformed by transmitting entity 310 on that stream. For example, aTurbo decoder or a Viterbi decoder may be used for decoder 516 if Turboor convolutional coding, respectively, is performed at transmittingentity 310. Decoder 516 provides a decoded data packet for each datasymbol block.

C. MIMO-OFDM System

With OFDM, up to N_(F) modulation symbols may be transmitted on theN_(F) subbands in each OFDM symbol period. Prior to transmission, thesemodulation symbols are transformed to the time-domain using anN_(F)-point inverse fast Fourier transform (IFFT) to generate a“transformed” symbol that contains N_(F) time-domain chips. To combatintersymbol interference (ISI), which is caused by frequency selectivefading, a portion (or N_(cp) chips) of each transformed symbol isrepeated to form a corresponding OFDM symbol. Each OFDM symbol istransmitted in one OFDM symbol period, which is N_(F)+N_(cp) chipperiods, where N_(cp) is the cyclic prefix length.

For a MIMO system that utilizes OFDM (i.e., a MIMO-OFDM system), thespatial spreading may be performed for each of the subbands used fordata transmission. Index m for transmission span is thus substitutedwith k, n for subband k and OFDM symbol period n. One vector s(k, n) maybe formed for each subband k in each OFDM symbol period n. Each vectors(k, n) contains up to N_(S) data symbols for transmission via the N_(S)spatial channels of H_(eff) (k, n) for subband k in OFDM symbol periodn. Up to N_(F) vectors, s(k, n) for k=1 . . . N_(F), may be transmittedconcurrently on the N_(F) subbands in one OFDM symbol period.

The set of N_(D) data symbol blocks may be transmitted in variousmanners in the MIMO-OFDM system. For example, each data symbol block maybe transmitted as one entry of the vector s(k, n) for each of the N_(F)subbands. In this case, each data symbol block is transmitted on allN_(F) subbands and achieves frequency diversity. Each data symbol blockmay further span one or multiple OFDM symbol periods. Each data symbolblock may thus span frequency and/or time dimensions (by system design)plus spatial dimension (with spatial spreading).

The steering matrices may also be selected in various manners for theMIMO-OFDM system. The steering matrices for the subbands may be selectedin a deterministic, pseudo-random, or permutated manner, as describedabove. For example, the L steering matrices in the set may be cycledthrough and selected in sequential order for subbands 1 through N_(F) inOFDM symbol period n, then subbands 1 through N_(F) in OFDM symbolperiod n+1, and so on. The transmission span may be defined to cover oneor multiple subbands and one or multiple OFDM symbol periods. The numberof steering matrices in the set may be less than, equal to, or greaterthan the number of subbands. The three cases described above forL=N_(M), L<N_(M), and L>N_(M) may also be applied for the subbands, withN_(M) being replaced with N_(F).

For the MIMO-OFDM system, each transmitter unit 332 performs OFDMmodulation on the transmit symbols for all N_(F) subbands of anassociated transmit antenna to obtain a corresponding stream of OFDMsymbols. Each transmitter unit 332 further conditions the OFDM symbolstream to generate the modulated signal. Each receiver unit 354 performsthe complementary OFDM demodulation on its received signal to obtain thereceived data symbols and received pilot symbols. OFDM modulation anddemodulation are known in the art and not described herein.

D. Steering Matrices Generation

The steering matrices used for spatial spreading should be unitarymatrices and satisfy the following condition:

V ^(H)(i)·V(i)=I, for i=1 . . . L,  Eq (5)

where “^(H)” denotes the conjugate transpose. Each steering matrixincludes N_(S) columns and may be expressed as V(i)=[v₁ (i) v₂(i) . . .v_(N) _(S) (i)]. For a given steering matrix V(i), the condition inequation (5) indicates that (1) each column of V(i) should have unitlength, or ∥v_(a)(i)∥=v_(a) ^(H)(i)·v_(a)(i)=1 for a=1 . . . N_(S), and(2) the Hermitian inner product of any two columns of V(i) should bezero, or v_(a) ^(H)(i)·v_(b)(i)=0 for a=1 . . . N_(S), b=1 . . . N_(S),and a≠b. This condition ensures that the N_(S) data symbols transmittedsimultaneously using steering matrix V(i) have the same power and areorthogonal to one another prior to transmission.

Some of the steering matrices may also be uncorrelated so that thecorrelation between any two uncorrelated steering matrices is zero or alow value. This condition may be expressed as:

C(ij)=V ^(H)(i)·V(j)≈0, for i=1 . . . L, j=1 . . . L, and i≠j,  Eq (6)

where C(ij) is the correlation matrix for V(i) and V(j) and 0 is amatrix of all zeros.

It may be difficult to satisfy the condition in equation (6) for allsteering matrices in the set. The steering matrices may be derived suchthat the maximum energy of the correlation matrices for all possiblepairs of steering matrices is minimized. The correlation matrix C(ij)for a given pair of steering matrices may be computed as shown inequation (6). The energy of C(ij) may be computed as

${{E({ij})} = {{{\underset{\_}{C}({ij})}}^{2} = {\sum\limits_{m = 1}^{N_{s}}{\sum\limits_{n = 1}^{N_{s}}{{c_{m,n}({ij})}}^{2}}}}},$

where c_(m,n)(ij) is the element in the m-th row and n-th column ofC(ij). The energy E(ij) is also (1) the trace of C^(H) (ij)·C(ij) and(2) the square of the Frobenius norm of C(ij). The steering matrices aregenerated such that the maximum energy E(ij) for all pairs of steeringmatrices is minimized.

The set of L steering matrices {V} may be generated in various manners,some of which are described below. The set of steering matrices may bepre-computed and stored at the transmitting and receiving entities andthereafter retrieved for use as they are needed. Alternatively, thesesteering matrices may be computed in real time as they are needed.

FIG. 6 shows an exemplary process 600 of a first scheme for generatingthe set of steering matrices {V}. Initially, index i is set to 1 for thefirst steering matrix to be generated (block 612). An N_(S)×N_(T) matrixG of random variables is then generated (block 614). The elements of Gare independent identically distributed (IID) complex Gaussian randomvariables each having zero mean and unit variance. An N_(T)×N_(T)correlation matrix of G is then computed as R=G^(H)·G (block 616).

Eigenvalue decomposition of the correlation matrix of G is nextperformed (block 618), as follows:

R=E·D·E ^(H),  Eq (7)

where E is an N_(T)×N_(S) unitary matrix of eigenvectors of R; and

-   -   D is an N_(S)×N_(S) diagonal matrix of eigenvalues of R.        The diagonal matrix D contains non-negative real values along        the diagonal and zeros elsewhere. These diagonal entries are        referred to as the eigenvalues of R and represent the power        gains for N_(S) eigenmodes of G.

The correlation between the eigenvector matrix E and each of thesteering matrices already generated for the set is then checked (block620). Block 620 is skipped for the first steering matrix. The check maybe achieved, for example, by (1) computing a correlation matrix C(j)between matrix E and each steering matrix V(j) already generated, forj=1 . . . (i−1), (2) computing the energy of each correlation matrixC(j) as described above, (3) comparing the energy of each correlationmatrix against a threshold, and (4) declaring low correlation if theenergies for all i−1 correlation matrices are less than the threshold.Other tests to check for low correlation may also be used, and this iswithin the scope of the invention. A determination is then made whetherthe low correlation criterion is met for the eigenvector matrix E (block622). The low correlation criterion is not met if the correlationbetween matrix E and any prior-generated steering matrix exceeds thethreshold. If this is the case, then the process returns to block 614 togenerate another matrix G. Otherwise, if the low correlation criterionis met, then steering matrix V(i) is set equal to matrix E (block 624).Steering matrix V(i) is a unitary matrix because matrix E is obtainedthrough eigenvalue decomposition, as shown in equation (7).

A determination is then made whether all L steering matrices for the sethave been generated (block 626). If the answer is ‘no’, then index i isincremented (block 628), and the process returns to block 614 togenerate the next steering matrix. Otherwise, the process terminates.

The steering matrices generated with process 600 may be improved by (1)identifying the pair of steering matrices with the highest energy fortheir correlation matrix and (2) “separating” these two steeringmatrices by pre-multiplying the steering matrices by unitary matrices(so that the resultant matrices are also unitary matrices). The unitarymatrices for the pre-multiplication may be selected to modify the twosteering matrices in a deterministic or random manner. The process maybe iterated until the maximum energy for the correlation matrix cannotbe reduced further.

In a second scheme, the set of L steering matrices is generated based ona set of (log₂ L)+1 independent isotropically distributed unitarymatrices. A random unitary matrix is isotropically distributed if itsprobability density is unchanged by pre-multiplication by anydeterministic N_(T)×N_(T) unitary matrix. Index i for the steeringmatrices in the set may be denoted as i=l₁l₂ . . . l_(Q), where Q=log₂L, l₁ is the first bit of index i, l_(Q) is the last bit of index i, andeach bit can take on a value of either 0 or 1. The L steering matricesmay then be generated as follows:

V(l _(l) l ₂ . . . l _(Q))=Ω₁ ^(l) ¹ ·Ω₂ ^(l) ² · . . . ·Ω_(Q) ^(l) ^(Q)·V ₀, for l ₁ ,l ₂ , . . . ,l _(Q)ε{0,1},  Eq(8)

where

-   -   V₀ is an N_(T)×N_(S) independent isotropically distributed        unitary matrix; and    -   Ω_(j) ^(l) ^(j) , for j=1 . . . Q, is an N_(T)×N_(T) independent        isotropically distributed unitary matrix.        Matrix V₀ may be defined, for example, as V₀ ^(T)=[I_(N) _(S)        0], where I_(N) _(S) is an N_(S)×N_(S) identity matrix. The        second scheme is described in further detail by T. L. Marzetta        et al. in “Structured Unitary Space-Time Autocoding        Constellations,” IEEE Transaction on Information Theory, Vol.        48, No. 4, April 2002.

In a third scheme, the set of L steering matrices is generated bysuccessively rotating an initial unitary steering matrix V(1) in anN_(T)-dimensional complex space, as follows:

V(i+1)=Θ^(i) ·V(1), for i=1 . . . L−1,  Eq (9)

where Θ^(i) is an N_(T)×N_(T) diagonal unitary matrix that may bedefined as:

$\begin{matrix}{{{\underset{\_}{\Theta}}^{i} = \begin{bmatrix}e^{j\; 2\; {\pi \cdot u_{1} \cdot {i/L}}} & 0 & \ldots & 0 \\0 & e^{j\; 2\; {\pi \cdot u_{2} \cdot {i/L}}} & \ldots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \ldots & e^{j\; 2\; {\pi \cdot u_{N_{T}} \cdot {i/L}}}\end{bmatrix}},} & {{Eq}\mspace{14mu} (10)}\end{matrix}$

and u₁, u₂, . . . u_(N) _(T) are N_(T) different values, each within therange of 0 to L−1, which are chosen such that the correlation betweenthe resulting steering matrices generated with the matrix Θ^(i) is aslow as possible. The N_(T) diagonal elements of Θ^(i) are L-th roots ofunity. The initial unitary steering matrix V(1) may be formed with N_(S)different columns of an N_(T)×N_(T) Fourier matrix D, where the (n,m)-th entry, w_(n,m), is given as:

$\begin{matrix}{{w_{n,m} = e^{{- j}\; 2\pi \frac{{({n - 1})}{({m - 1})}}{N_{T}}}},{{{for}\mspace{14mu} n} = {{\left\{ {1\mspace{14mu} \ldots \mspace{14mu} N_{T}} \right\} \mspace{14mu} {and}\mspace{14mu} m} = \left\{ {1\mspace{14mu} \ldots \mspace{14mu} N_{T}} \right\}}},} & {{Eq}\mspace{14mu} (11)}\end{matrix}$

where n is a row index and m is a column index. The third scheme isdescribed in further detail by B. M. Hochwald et al. in “SystematicDesign of Unitary Space-Time Constellations,” IEEE Transaction onInformation Theory, Vol. 46, No. 6, September 2000.

In a fourth scheme, the set of L steering matrices is generated with abase matrix B and different scalars. The base matrix may be a Walshmatrix, a Fourier matrix, or some other matrix. A 2×2 Walsh matrix beexpressed as

${\underset{\_}{W}}_{2 \times 2} = {\begin{bmatrix}1 & 1 \\1 & {- 1}\end{bmatrix}.}$

A larger size Walsh matrix W_(2N×2N) may be formed from a smaller sizeWalsh matrix W_(N×N) as follows:

$\begin{matrix}{{\underset{\_}{W}}_{2N \times 2N} = {\begin{bmatrix}{\underset{\_}{W}}_{N \times N} & {\underset{\_}{W}}_{N \times N} \\{\underset{\_}{W}}_{N \times N} & {- {\underset{\_}{W}}_{N \times N}}\end{bmatrix}.}} & {{Eq}\mspace{14mu} (12)}\end{matrix}$

Walsh matrices have dimensions that are powers of two. Fourier matricesof any square dimension (e.g., 2, 3, 4, 5, and so on) may be formed asshown in equation (11).

An N_(T)×N_(T) Walsh matrix W, Fourier matrix D, or some other matrixmay be used as the base matrix B to form other steering matrices. Eachof rows 2 through N_(T) of the base matrix may be independentlymultiplied with one of M different possible scalars, where M>1. M^(N)^(T) ⁻¹ different steering matrices may be obtained from M^(N) ^(T) ⁻¹different permutations of the M scalars for the N_(T)−1 rows. Forexample, each of rows 2 through N_(T) may be independently multipliedwith a scalar of +1, −1, +j, or −j, where j=√{square root over (−1)}.For N_(T)=4 and M=4, 64 different steering matrices may be generatedfrom the base matrix B with the four different scalars. Additionalsteering matrices may be generated with other scalars, e.g., e^(±j3π/4),e^(±jπ/4), e^(±jπ/8), and so on. In general, each row of the base matrixmay be multiplied with any scalar having the form e^(jθ), where θ may beany phase value. N_(T)×N_(T) steering matrices may be generated asV(i)=g_(N) _(T) ·B(i), where g_(N) _(T) =1/√{square root over (N_(T))}.and B(i) is the i-th matrix generated with the base matrix B. Thescaling by g_(N) _(T) ensures that each column of V(i) has unit power.

Other schemes may also be used to generate the set of steering matrices,and this is within the scope of the invention. In general, the steeringmatrices may be generated in a pseudo-random manner (e.g., such as thefirst scheme) or a deterministic manner (e.g., such as the second andthird schemes).

E. Performance

FIG. 7 shows plots of the cumulative distribution function (CDF) of theoverall spectral efficiency achieved for an exemplary MIMO system. Forthis MIMO system, the transmitting entity is equipped with four transmitantennas (N_(T)=4) and the receiving entity is equipped with fourreceive antennas (N_(R)=4). The MIMO channel is assumed to be asdescribed above for equation (1). The received SNR, which is the SNR ofthe received symbols prior to the receiver spatial processing, isassumed to be 20 dB. The receiving entity is assumed to be using aminimum mean square error (MMSE) receiver spatial processing technique.

Plot 710 shows the CDF of the overall spectral efficiency for the casein which spatial spreading is not performed. Spectral efficiency isgiven in units of bits per second per Hertz (bps/Hz). For a givenspectral efficiency x, the CDF indicates the probability of the overallspectral efficiency being worse than x. For example, point 712 indicatesthat there is a one percent (10⁻²) probability of the overall spectralefficiency being worse than 9 bps/Hz without spatial spreading. If thetransmitting entity encodes and transmits data at an overall rate of 9bps/Hz, then there is a one percent probability that the receivingentity will not be able to correctly decode the data. This probabilityis also commonly referred to as the “outage” probability.

Plots 720, 730 and 740 show the CDFs of the overall spectral efficiencyachieved with spatial spreading using 4, 16 and 64 steering matrices,respectively. Points 722, 732 and 742 indicate that there is a onepercent probability of the overall spectral efficiency being worse than12.5, 14.6 and 15.8 bps/Hz, respectively, with 4, 16 and 64 steeringmatrices, respectively. For one percent outage probability, the use ofspatial spreading improves the overall spectral efficiency from 9 bps/Hzto approximately 15.8 bps/Hz (with 64 steering matrices) for theexemplary MIMO system. Line 750 is for 50% probability and may bereferenced to determine the average overall spectral efficiency for thefour cases.

FIG. 7 shows the performance for an exemplary MIMO system with somespecific assumptions. In general, the amount of improvement may bedependent on various factors such as, for example, the characteristicsof the MIMO channel, the number of transmit and receive antennas, thespatial processing technique used at the receiving entity, the codingand modulation schemes used for data transmission, and so on.

2. MISO System

A MISO system employs multiple (N_(T)) transmit antennas at atransmitting entity and a single receive antenna at a receiving entityfor data transmission. A MISO channel formed by the N_(T) transmitantennas and the single receive antenna is composed of a single spatialchannel. The MISO channel may be characterized by a 1×N_(T) channelresponse row vector h, which is h=[h₁ h₂ h_(N) _(T) ], where entryh_(j), for j=1 . . . N_(T), denotes the coupling between transmitantenna j and the single receive antenna.

Spatial spreading may be used to randomize an effective MISO channelobserved by the single-antenna receiving entity so that performance isnot dictated by the worst-case channel conditions. For the MISO system,the transmitting entity performs spatial processing with a set ofsteering vectors.

The spatial processing at the transmitting entity for spatial spreadingin the MISO system may be expressed as:

x _(miso)(m)=v(m)·s(m),  Eq (13)

where

-   -   s(m) is a data symbol to be sent in transmission span m;    -   v(m) is an N_(T)×1 steering vector for transmission span m; and    -   x_(miso) (m) is an N_(T)×1 vector with N_(T) transmit symbols to        be sent from the N_(T) transmit antennas in transmission span m.

A set of L steering vectors may be generated and denoted as {v}, or v(i)for i=1 . . . L. One steering vector in the set may be selected for eachtransmission span m (e.g., in a pseudo-random or deterministic manner,similar to that described above for the steering matrices). Thetransmitting entity performs spatial processing for each transmissionspan m with the steering vector v(m) selected for that transmissionspan.

The received symbols at the receiving entity with spatial spreading maybe expressed as:

r(m)=h(m)·v(m)·s(m)+n(m)=h _(eff)(m)·s(m)+n(m),  Eq (14)

where

-   -   r(m) is a received symbol for transmission span m;    -   h_(eff) (m) is an effective channel response for transmission        span m, which is h_(eff) (m)=h(m)·v(m); and    -   n(m) is the noise for transmission span m.

As shown in equation (14), because of the spatial spreading performed bythe transmitting entity, a data symbol stream observes the effectivechannel response h_(eff) (m), which includes the actual channel responseh(m) and the steering vector v(m). The receiving entity can performdetection (e.g., matched filtering or equalization) on the receivedsymbols r(m) with an effective channel response estimate ĥ_(eff)(m) toobtain detected symbols ŝ(m), as is known in the art. The receivingentity further processes (e.g., demodulates, deinterleaves, and decodes)the detected symbols r(m) to obtain decoded data.

The steering vectors used for spatial spreading in the MISO systemshould have equal energy (e.g., ∥v(i)∥²=v^(H) (i)·v(i)=1 for i=1 . . .L) so that the transmit power used for the data symbols is not varied bythe spatial spreading. Some of the steering vectors may also beuncorrelated so that the correlation between any two uncorrelatedsteering vectors is zero or a low value. This condition may be expressedas:

c(ij)=v ^(H)(i)·v(j)≈0, for i=1 . . . L, j=1 . . . L, and i≠j,  Eq (15)

where c(ij) is the correlation between steering vectors v(i) and v(j).

The set of L steering vectors may be generated in various manners (e.g.,in a pseudo-random or deterministic manner, similar to that describedabove for the steering matrices). The columns of the steering matricesgenerated as described above may be used for the steering vectors forspatial spreading.

The spatial spreading techniques described herein may be implemented byvarious means. For example, these techniques may be implemented inhardware, software, or a combination thereof. For a hardwareimplementation, the processing units used to perform spatial spreadingat the transmitting entity may be implemented within one or moreapplication specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedherein, or a combination thereof. The processing units used to performspatial processing at the receiving entity may also be implementedwithin one or more ASICs, DSPs, processors, and so on.

For a software implementation, the spatial spreading techniques may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. The software codes may be storedin memory units (e.g., memory units 342 and 382 in FIG. 3) and executedby a processor (e.g., controllers 340 and 380). The memory unit may beimplemented within the processor or external to the processor, in whichcase it can be communicatively coupled to the processor via variousmeans as is known in the art.

Headings are included herein for reference and to aid in locatingcertain sections. These headings are not intended to limit the scope ofthe concepts described therein under, and these concepts may haveapplicability in other sections throughout the entire specification.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A wireless communication apparatus, comprising: adata processor configured to process data to obtain at least one blockof data symbols; and a spatial processor configured to spatially processthe at least one block of data symbols with a plurality of steeringmatrices to obtain a plurality of sequences of transmit symbols for aplurality of transmit antennas, wherein the plurality of steeringmatrices randomize an effective MIMO channel observed by a receivingentity for the at least one block of data symbols. wherein the spatialprocessor is configured to partition the at least one block of datasymbols into a plurality of subblocks of data symbols, wherein thespatial processor is configured to select a steering matrix for eachsubblock of data symbols, wherein the spatial processor is configured tospatially process each subblock of data symbols with the steering matrixselected for the subblock, and wherein the partitioning the at least oneblock of data symbols comprises partitioning a single block of datasymbols into a plurality of subblocks of data symbols.